{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM加高斯函数解决分类问题_gamma很关键\n",
    "## 关键使用地模型\n",
    "\n",
    "```python\n",
    "from sklearn.svm import LinearSVC # 1.不支持核函数，使用方法见11.4\n",
    "from sklearn.svm import SVC # 2.支持核函数，SVC即Support Vector Classify，使用方法见本节\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 高斯核函数中的γ\n",
    "![高斯核函数中的γ](images/高斯核函数中的γ.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v+J5Ls1Zq+43wkRfB+1a0fnux+7E1JhC4vdJssW7NN6/7nVV84c7dQ/WnlWb7jfCVd8CBh4Bo/f7KO9IKBjUMVI2affTPbt7B9T94iCMRLJG44pyT+PNLXzyBEprVV9Kzl37kRVkQ6LD8pNYSqFWbD1SH287f0mXJLMPa+NlHb75rN1+4c/eTc7IfieALd+4e+S7n5rt2p1t1broa3pGlJOn8/wO7htteti0bFgYBaD3fsqGa8gyokEAg6RJJ90raKWl9l/1/IunH2eL1WyQ9t23fEUnbsp/Nna8tSpFpcqOkojZeWRfnOjQdJC7p/rTlq4fbXrbUA1WOsQOBpCXAx4BXAS8ArpD0go7D7gJmI+IM4CbgQ237DkbEmdnPa8YtT54i73I8dH5IZV6ca3pHlpKk+9MuuKbV1NJu6bLW9hSkHqhyFFEjOBvYGREPRMRjwA3AmvYDIuJbEfFo9vR2WovUl6rIu5ykq84pKvPiXNM7spQknf9/xmWt9vblJwFq/U6k/R1IP1DlKGIcwSqgvfdmF3BOj+OvBL7e9vwZkuZorWe8MSJu7vYiSeuAdQAzMzNDF/Lqi09fMJQeRr/L8dD5IZV5cV6+OqczMe07stQknf9/xmXpXPg7zZdry4bW53v56lYQSLW8mSICgbps65qKJOlNwCzw+22bZyJij6RTga2SdkTE/Yv+YMQmYBO0soaGLeQgqzYNOhdRkUGlEcq8OF9wTfesjcTvyFLndQWGkHKgylFEINgFnNT2fDWwp/MgSRcC7wF+PyIOzW+PiD3Z7wckfRs4C1gUCIrQ6y5nmMm3vBTgkCZ9cd5+48I7sJe8Ee77Rq3uyFLmdQWm39jjCCQ9DfgpcAGwG7gDeGNE3NN2zFm0OokviYj72ravBB6NiEOSjgO+D6yJiB/3es9RxxH0kkLu9FTfdXVerIu6OCeetz0NUvhuJG9Sn++C5Y0jGLtGEBGPS3o7cCuwBLg2Iu6RtAGYi4jNwIeBZwKflwTw8yxD6PnAxyU9QavjemO/IDApVXcAT/1d16Sqy706ohP8ItZR1d+N5HXejMxnxUFtPoOFTDoXEbcAt3Rsu6bt8YU5r/sekMTQ3qo7gHulpE5FIBjUsHdWzhKauKq/G8kb9GYk4VpDY0YW91N17rTvuhhtvEFN87brpOrvRvIGuRlJfKCjA0Gm6tzppEdzlmWU8QY1zduuk6q/G8kb5GYk8YGOjVmPYBBV5k47JZXRmnlqmrddN0mPK6jaIFlxiTdhOhAkwimpjD7eoIZ52zZFBrkZSXygY6OmobbEORXUplUin+3GT0NtNZD6PDJmo1rw2Qa05Kk+ggQ6jN00ZGlxM0/SpnrQ46TNf64THHPgGoGNzwvBNILX4ShAotlDDgQ2nsTzo604XoejAIlmDzkQTEhjlrJM9A7HiudBjwVIdACkA8EENKoKnegdjhXPgx4LkOgASAeCCWhUFTrROxwrnqeaKECimXHOGpqARlWhvRBMkiaR3eNBjwVJMDPOgWACGjVbYypTPCQ8s2PZJjmluaeamE4OBBPQuHmDqr7DmYL54IvkKc1tWIX0EUi6RNK9knZKWt9l/9MlfS7b/wNJJ7fte3e2/V5JFxdRnqp5tsaSOXNpgUY1TVohxq4RSFoCfAx4Ja31i++QtLljpbErgV9FxL+SdDnwQeCPJL0AuBx4IXAi8E1J/zoiFt7O1JCr0CVy5tICjWqatEIUUSM4G9gZEQ9ExGPADcCajmPWANdlj28CLlBrzco1wA0RcSgiHgR2Zn/PbHDOXFrA2T02rCL6CFYB7fOr7gLOyTsmW+P4AHBstv32jtd2vY2WtA5YBzAzM1NAsW1qNDhzqVd2kLN7bFBFBAJ12dY5t3XeMYO8trUxYhOwCVrTUA9TQJtyqWQulaxfdpAv/DaoIgLBLuCktuergT05x+yS9DRgOfDIgK8166/qzKUKODvIilJEH8EdwGmSTpF0NK3O380dx2wG1maPXw9sjdaKOJuBy7OsolOA04AfFlAms6nn7CArytg1gqzN/+3ArcAS4NqIuEfSBmAuIjYDnwL+TtJOWjWBy7PX3iPpRuDHwOPA26YhY8isDM4O6sODDAfmpSqtGv6Sjq2zjwBa2UEes0IyS0MuKE8Cn3cvVWnp8BoGhfDAxR5SGmRYg8+7p5iw8vX6krpWMBRnB+VIaZBhDT7vrhFY+Sb9JfXSmZbSIMOUglIOBwIr3yS/pDWohlsJUloAJqWglMOBwMo3yS9pSm3DVp2UFoBJKSjlcB+BlW+SI4FrUA23kqQyyLAGI98dCKwak/qSLl+dNQt12W5WhFFSQVMJSjncNGT1MUgncA2q4VZjU9oH5UBg9TDoFzCltmGbPlPaB+WmIauHYXKxE6+GVyKRka21N6V9UA4EVg9T+gUshdd0Hl1nAF22Eg4+svi4mvdBuWnI6qEGudjJmtLmjInr1hz52G/gqKULj5uCPigHAquHUTuBUxllXGU5cmtTD3nkdS/dAuiRx+Dpvz11fVBuGrJ6GCUXO5UmkVHKUWSbfl5K7aBlqaMizl9eAD34K3jXg+OXMSGuEVh9nHEZvPNueN/+1u9+X+xUmkSGLUfRKYrdalODlmVUVdaAijp/DWqOdCCw6ZVKB/Ow5Sg6gC1IqR2yjKPIuxB/9U/GCw6DBpeizl+DxqSMFQgkHSPpNkn3Zb9XdjnmTEnfl3SPpO2S/qht32ckPShpW/Zz5jjlMVsglTu6ZYu+Fr3LMYkANl+bygsGRZ6TvAvx3LWj36UPc5df1Plr0JiUcWsE64EtEXEasCV73ulR4M0R8ULgEuCvJK1o2391RJyZ/WwbszxmT0nhjm77jXDo14u3Lzk6vxyTDGBlnJPcC27HaojD3KUPc5c/7PnrVdMYtjmypsYNBGuA67LH1wGXdh4QET+NiPuyx3uAh4Hjx3xfs/5SuKPbsgGeOLx4+9HPzC/HJC/WZZyTYQLWoHfpw9zlD3P+pnTKiGGNmzX07IjYCxAReyU9q9fBks4Gjgbub9v8XyRdQ1ajiIhDOa9dB6wDmJmZGbPY1hhVjzLulXmSZ9KzVU76nFxwzeL1ghGLagQweNAYZjLBYc5fDVYPK0PfQCDpm8Bzuux6zzBvJOkE4O+AtRHxRLb53cD/oRUcNgHvArrWFSNiU3YMs7OzXT5RZgkadTbUqgPYOLpdiE+7CH703xcvJj9oLadbcOn1+kHPXyoJBRXrGwgi4sK8fZJ+IemErDZwAq1mn27H/Uvga8CfRcTtbX97b/bwkKRPA386VOnNUjfsBWxadLsQz7xs9FrOpGpJnrYcGL9paDOwFtiY/f5y5wGSjga+BHw2Ij7fsW8+iIhW/8LdY5bHLC01WJSkNOPWciZRS2pqoO6giNFbWSQdC9wIzAA/B94QEY9ImgWuioi3SnoT8GngnraXviUitknaSqvjWMC27DW/6fe+s7OzMTc3N3K5zcye1KCZWSXdGRGzi7aPEwiq4kBglWvQxcOmR14g8FxDZsNKZQ4js4J4igmzYaUyh5FZQRwIzIbllEObMm4aMuunIatUDcR9I1PJgcCsl279AUctbc0VdOSxp45rQsqh+0amlpuGzHrp1h/wxOHWXEF1m5Vy3DUC3DcytVwjMOtlWlapKuJu3n0jU8s1ArNeUlnTYFxF3M1Py7mwRRwIzHpJYU2DIhRxNz8t58IWcSCwZhq0vTyFNQ2KUMTd/LScC1vEU0xY83S2l0PrznaaL2pN/DfbInlTTLhGYM3TxOwX381bD84asuZpavZL3jTOHiTWeK4RWPM4++UpXrPXcCCwJnL2y1Oa2Exmi4wVCCQdI+k2Sfdlv1fmHHdE0rbsZ3Pb9lMk/SB7/eey1czMJsvt5U+pazPZuKOkbYFx+wjWA1siYqOk9dnzd3U57mBEnNll+weBj0TEDZL+FrgS+Jsxy2TWX50Xhy9SHdfs9ZxHhRu3aWgNcF32+Dpa6w4PJFun+HzgplFeb1aaab77rGMzmZuzCjduIHh2ROwFyH4/K+e4Z0iak3S7pPmL/bHA/oh4PHu+C1iV90aS1mV/Y27fvn1jFttsQNPemVrHZrK6NmclrG/TkKRvAs/psus9Q7zPTETskXQqsFXSDuCfuxyXO7otIjYBm6A1oGyI9zYbXa+7z5QvlsOoWzNZHZuzEtc3EETEhXn7JP1C0gkRsVfSCcDDOX9jT/b7AUnfBs4CvgCskPS0rFawGtgzwr/BbHJ895meC67pPko65easxI3bNLQZWJs9Xgt8ufMASSslPT17fBxwLvDjaM1t8S3g9b1eb1YpjzlITx2bsxI31lxDko4FbgRmgJ8Db4iIRyTNAldFxFslvRz4OPAErcDzVxHxqez1pwI3AMcAdwFviohD/d7Xcw1ZaTxHz3g8ajkpeXMNedI5s358MRuNg2hy8gKB5xoy66dunampaEJH+5TwFBNmNhnuaK8NBwIzG0/egDt3tNeGA4GZja7XgLs6jlpuKAcCMxtdv34Ap3nWgjuLzWx0/foBhulod3ZWZVwjMLPRFdUPMO1zOiXOgcDMRldUP4BnFK2UA4FZHaQ6FXZR/QBONa2U+wjMUpf6QixFDLjzjKKVco3ALHVNaDZxqmmlHAjMUpfbbPJQWs1E43CqaaXcNGSWurxmE0ivmWgcntOpMq4RmA2iys7abs0m7aatmchK5xqBWT9Vd9bOv8eWDT1qBs6usdGNVSOQdIyk2yTdl/1e2eWY8yRta/v5f/ML2Ev6jKQH2/adOU55zCYihc7aMy6Dd96dtaF34ewaG8O4TUPrgS0RcRqwJXu+QER8KyLOjIgzgfOBR4FvtB1y9fz+iNg2ZnnMipdSjruza2wCxg0Ea4DrssfXAZf2Of71wNcj4tEx39esPGVOp9yvL8LZNTYB465ZvD8iVrQ9/1VELGoeatu/FfjLiPhq9vwzwO8Ch8hqFHlrFktaB6wDmJmZ+Z2f/exnI5fbbChlLbnopR1twvKWquxbI5D0TUl3d/lZM2QBTgBeDNzatvndwPOAf0NrAft35b0+IjZFxGxEzB5//PHDvLXZeMq6C0+hL8IaqW/WUERcmLdP0i8knRARe7ML/cM9/tRlwJci4nDb396bPTwk6dPAnw5YbrNylZHjnlJfhDXKuH0Em4G12eO1wJd7HHsFcH37hix4IEm0+hfuHrM8ZvXlpR2tIuMGgo3AKyXdB7wye46kWUmfnD9I0snAScD/7Hj930vaAewAjgP+fMzymNWXM4KsImMNKIuIXwIXdNk+B7y17fk/Aqu6HHf+OO9vNlUWDBzzKl1WHo8sNkuJ59uxCniuITOzhnMgMDNrOAcCM7OGcyAwM2s4BwIzs4ZzIDAzazgHAjOzhhtr9tGqSNoHVD396HHAP1VchmG4vJPl8k6Wy1uM50bEolk7axkIUiBprtt0rqlyeSfL5Z0sl3ey3DRkZtZwDgRmZg3nQDC6TVUXYEgu72S5vJPl8k6Q+wjMzBrONQIzs4ZzIDAzazgHggFJeoOkeyQ9ISk3LUzSJZLulbRT0voyy9hRjmMk3Sbpvuz3ypzjjkjalv1srqCcPc+XpKdL+ly2/wfZaneVGaC8b5G0r+2cvrXb3ymDpGslPSyp6xKwavlo9m/ZLumlZZexozz9yvsKSQfazm2lS7dJOknStyT9JLs2/KcuxyR1jnNFhH8G+AGeD5wOfBuYzTlmCXA/cCpwNPAj4AUVlfdDwPrs8XrggznH/abCc9r3fAF/DPxt9vhy4HOJl/ctwH+rqowdZfk94KXA3Tn7Xw18HRDwMuAHiZf3FcBXqz6vbeU5AXhp9vi3gZ92+TwkdY7zflwjGFBE/CQi7u1z2NnAzoh4ICIeA24A1ky+dF2tAa7LHl8HXFpROXoZ5Hy1/ztuAi6QpBLL2C6l/9++IuI7wCM9DlkDfDZabgdWSDqhnNItNkB5kxIReyPiH7LHvwZ+wuIleZM6x3kcCIq1Cnio7fkuuqzVXJJnR8ReaH1ggWflHPcMSXOSbpdUdrAY5Hw9eUxEPA4cAI4tpXSLDfr/+7qsGeAmSSeVU7SRpPR5HdTvSvqRpK9LemHVhZmXNVmeBfygY1ctzrHXLG4j6ZvAc6pZVLgAAAIRSURBVLrsek9EfHmQP9Fl28Tyc3uVd4g/MxMReySdCmyVtCMi7i+mhH0Ncr5KPad9DFKWrwDXR8QhSVfRqs2cP/GSjSalczuIf6A1V85vJL0auBk4reIyIemZwBeA/xwR/9y5u8tLkjvHDgRtIuLCMf/ELqD9DnA1sGfMv5mrV3kl/ULSCRGxN6uKPpzzN/Zkvx+Q9G1adzVlBYJBztf8MbskPQ1YTnXNB33LGxG/bHv6CeCDJZRrVKV+XsfVfpGNiFsk/bWk4yKissndJC2lFQT+PiK+2OWQWpxjNw0V6w7gNEmnSDqaVudm6Zk4mc3A2uzxWmBRjUbSSklPzx4fB5wL/Li0Eg52vtr/Ha8HtkbWC1eBvuXtaP99Da1241RtBt6cZba8DDgw35yYIknPme8fknQ2revXL3u/aqLlEfAp4CcR8Zc5h9XjHFfdW12XH+Df0Yruh4BfALdm208Ebmk77tW0sgfup9WkVFV5jwW2APdlv4/Jts8Cn8wevxzYQSv7ZQdwZQXlXHS+gA3Aa7LHzwA+D+wEfgicWvHnoF95PwDck53TbwHPq7Cs1wN7gcPZZ/dK4Crgqmy/gI9l/5Yd5GTDJVTet7ed29uBl1dc3n9Lq5lnO7At+3l1yuc478dTTJiZNZybhszMGs6BwMys4RwIzMwazoHAzKzhHAjMzBrOgcDMrOEcCMzMGu7/A8wmoSM0XtHFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "X, y = datasets.make_moons(noise=0.15, random_state=666)\n",
    "plt.scatter(X[y==0, 0], X[y==0, 1])\n",
    "plt.scatter(X[y==1, 0], X[y==1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "def RBFKernelSVC(gamma=1.0):\n",
    "    return Pipeline([\n",
    "        ('std_scaler', StandardScaler()),\n",
    "        ('svc', SVC(kernel='rbf', gamma=gamma)) # rbf表示高斯核\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('std_scaler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('svc',\n",
       "                 SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "                     decision_function_shape='ovr', degree=3, gamma=1.0,\n",
       "                     kernel='rbf', max_iter=-1, probability=False,\n",
       "                     random_state=None, shrinking=True, tol=0.001,\n",
       "                     verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = RBFKernelSVC(gamma=1.0)\n",
    "svc.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_decision_boundary(model, axis):\n",
    "    \"\"\"\n",
    "    根据模型绘制决策边界\n",
    "    \"\"\"\n",
    "    x0, x1 = np.meshgrid(\n",
    "        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1, 1),\n",
    "        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1, 1),\n",
    "    )\n",
    "    X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "\n",
    "    y_predict = model.predict(X_new)\n",
    "    zz = y_predict.reshape(x0.shape)\n",
    "\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])\n",
    "    \n",
    "    plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: UserWarning: The following kwargs were not used by contour: 'linewidth'\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(svc, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0, 0], X[y==0, 1])\n",
    "plt.scatter(X[y==1, 0], X[y==1, 1])\n",
    "plt.show() # 和多项式差不多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 调大gamma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('std_scaler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('svc',\n",
       "                 SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "                     decision_function_shape='ovr', degree=3, gamma=10,\n",
       "                     kernel='rbf', max_iter=-1, probability=False,\n",
       "                     random_state=None, shrinking=True, tol=0.001,\n",
       "                     verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = RBFKernelSVC(gamma=10)\n",
    "svc.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: UserWarning: The following kwargs were not used by contour: 'linewidth'\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(svc, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0, 0], X[y==0, 1])\n",
    "plt.scatter(X[y==1, 0], X[y==1, 1])\n",
    "plt.show() # gamma取值越大，决策边界越收紧，可以看出太大不好，过拟合了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('std_scaler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('svc',\n",
       "                 SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "                     decision_function_shape='ovr', degree=3, gamma=100,\n",
       "                     kernel='rbf', max_iter=-1, probability=False,\n",
       "                     random_state=None, shrinking=True, tol=0.001,\n",
       "                     verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = RBFKernelSVC(gamma=100)\n",
    "svc.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: UserWarning: The following kwargs were not used by contour: 'linewidth'\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(svc, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0, 0], X[y==0, 1])\n",
    "plt.scatter(X[y==1, 0], X[y==1, 1])\n",
    "plt.show() # gamma取值越大，决策边界越收紧，可以看出太大不好，过拟合了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('std_scaler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('svc',\n",
       "                 SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "                     decision_function_shape='ovr', degree=3, gamma=0.5,\n",
       "                     kernel='rbf', max_iter=-1, probability=False,\n",
       "                     random_state=None, shrinking=True, tol=0.001,\n",
       "                     verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = RBFKernelSVC(gamma=0.5)\n",
    "svc.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: UserWarning: The following kwargs were not used by contour: 'linewidth'\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(svc, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0, 0], X[y==0, 1])\n",
    "plt.scatter(X[y==1, 0], X[y==1, 1])\n",
    "plt.show() # gamma越小，决策边界越松弛，当很小时，可以任务趋于无穷大成一条直线了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('std_scaler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('svc',\n",
       "                 SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "                     decision_function_shape='ovr', degree=3, gamma=0.1,\n",
       "                     kernel='rbf', max_iter=-1, probability=False,\n",
       "                     random_state=None, shrinking=True, tol=0.001,\n",
       "                     verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = RBFKernelSVC(gamma=0.1)\n",
    "svc.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: UserWarning: The following kwargs were not used by contour: 'linewidth'\n"
     ]
    },
    {
     "data": {
      "image/png": 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3aZlEQ0BSjWWTwQVVCfcO7Zi9GQw6qk4XPe+53cvun3DK3AyXPfoZgEpPvrG2qwitDAgA1rNKZO/3WalgLQ51u+FOFYvGdQ/t/Mk37k7dpsp00a1P3rzsZjoAKxfn2PrkzZWeeGNtVxFaGRBueXma+Z4AMD/COvIqWBuujBP1yp8/yMlHdh+/ofvE4mFOPrKb1yCqoBDTyqExpov2u9dy1fdgjrVdRWhlQAhVV6CCtcHKOlGvmt17fB8d5vOsmt1baUCIKQD0ijFddHZqA6fMzaQ+X6VY21WEVgaEUHUFKlgbrKwT9YrFw7meD62INYKKFmO6aKz3YI61XUVoZUDIUleQZW5ABWuDlXWiPjaxhomU9zw2sSbofrrVMQj0ii1dNNZ7MMfariK0MiAMqyvIOjeggrXByjpRH53etmxoCsBtJUentwXbR1HpoDGJYRnrWO/BHGu7QmtlQIDBdQVZ5waKKFhrUtZSkSfq3snq11dtYeUbTwSdvC4jHTQWWsb6RG2pPejW2oAwSJ65gZAFa03LWppffTGvQfAso7TJ6hVH9/HaqVeO9d5tvnWklrFerk21B90UEFJUNTfQxKyl+dUXZzpJ50lPDTlZ3aZewCBaxnq5rLUHTetFKCCkqGpuoK1ZS3nTU8eZrG5zL2CQGOsSqpSl9qCJvQgFhBRVLWbX1qylvFf8WSar+2YBtbgXMEiMdQlVylJ70MQKZgWEPqpYzK6tWUt5r/j7TVZPbPsQay4Iszpo28RYl1ClLLUHTaxgVkCISFuX2c6bnto9WT2xeBhOOY2/O+c/8pRfqR7AGGKrS6hSltqDJlYwKyBEpo3LbOdNT12aCI53WQhphmG1B02sYFZAkMoNS0/VRHB5YihOq4tOsPjV/X/A1Pw/ArCwYqrKJo1NAUHGEmo10970VKWDlk/FaaNZeezo8bWTp+ZfrnWmkQKCjCzkaqbqBVRPxWn5NS3TSAGhYE1aiqLXKAViSgeNl4rT8mtappECQoGathRFrzzpoqFuFi/FUXFafk3LNFJAKFATl6LoNihdtA2rg8Yg5CSwitPya1qmkQJCgZq+FEVauuj8xBT3XPhHPLW/fuOndRN6EljFafk17V4JCggFavpSFPOrL+bvNv1KpX8MTVtcLI8iJoFVnJZfk+6VoIBQoKYuRdF7r+Cq/hiauLhYHpoEltCCBAQz+yDwp8AE8BV3/2zP6ycBtwMXAy8Bv+PuPw6x75g1YSmKmCeDm5byl5cmgSW0sQOCmU0AXwJ+HZgBvm9md7r7412bfQL4R3d/p5ldBXwO+J1x910HdVyKoi73C25ayl9emgSW0EL0EC4BDrj7MwBm9lXgCqA7IFwB/Ifk8W7gi2Zm7u4B9i8BLOsJ1CQjqGkpf4MMyibSJLCEEiIgbAB+2vX9DPDeftu4+4KZHQHeCrzYvZGZ7QB2AGxcuzZA02SQOgaBbk1L+etnWDaRAoCEEiIgWMpzvVf+WbbB3XcBuwA2b9qk3kNBjk8K1zAIdGtayl8/WlJCyhIiIMwAZ3V9vxF4vs82M2Y2CZwKvBxg35JBk9cJalLKXz/KJpKyhAgI3wfONbN3AM8BVwEf7dnmTpYWsP974ErgXs0fFC/GnkCb6wZGpWyiE+lzVIyxA0IyJ3AdcBdLaae3uvt+M7sJ2OfudwJ/AfxXMzvAUs/gqnH3K8vFnB7a0fa6gVEpm2i5WD5HTQxKQeoQ3H0PsKfnuRu7Hh8FPhxiX7JcHQJBR9vrBkalbKLlYvgcxRKUQlOlcg3VNTuoyLqBJl6tdVM20ZtiqD+JISgVQQGhJpZNDNcoCHQrqm6gqVdrki6G+pMYglIRVgzfRKp220XX1GJIaJj7z9/J/MTye86GqBsYdLUmzVPU5yiPfsGn7kWR6iFEqncBuSYoqm6gqVdrki6G+pOmFkUqIESkyfUCHUXUDcQwhCDlKuJzlGceKoagVAQFhIo1YW6gSFn+SJt6tSblGWUeqolFkQoIFahrllDZsv6RNvVqTcrT1KyhvBQQSlSnmoEY5PkjbeLVWl5NT70NqfdYTacMOUL75qEUEEoQ4xISdaDJ4uyUeptd2rFyjJT1Nls3D6WAUKAmZgqVSZPF2fXrTf2LR67jgw//nnoMXdKOleE4hnUFhTbOQykgBNaGTKGyjDpZXPXQSRX779drWuGLQHN6DCGObf8epvPK1MZWD7kpIAR020XXaFgooFEmi6seOhll/yFOcv16U91CT5KWHfhC/W779zw38pcf+EGYxtaUxboK9eZNm/y+nXF319QbiM/H792c+sf+Skl/7Hn333uSg6Ve0D0Xfj7XSS7tfdI4xhc+dCjz++bZ34K9hTcmVzM1fzh3gMgSXEL9bkMd81h9+pKTHnT3LaP8rHoII1C2ULyqnojOm60SKt2xtzfltuL4cFG3UPMvae2e9DeYnH8DyHf1nvXKP9TvVmnK/Skg5KBAEL8qJ6LPe2435MxWCRnAulNv+10Fh5okzdK+rIEta1DM+7sd1OtQmnI6BYQMlDZaH1VWLW998uZlWSodjvXdf1EBrOir4CxzFpAtcGQNinl+t1XPJdWVAsIAShutnyqHAwZlr/Tbf5EBrMir4LR2p8kS2LIGxTy/W1Uej0YBIYUmi+utquGAQdkr/dR1PLu33UdXnsbKhVkmff74NlkDW56gmPV3W/VcUl0pIHTR0JCMY9Sr/bqOZ/e2e9Q01CKCoooaR9P6gKDegIRS16v9UMYJbKGDolbAHU1rA8LxQKDegGSQ9eq3rlf7TdP24Dyq1gUEpY5KXspYqScF5/xaFRC0tISMQhkr0hatCAhKH5U82rxWftULA0q1Gh0QFAgkrzavla+hMVlRdQOKsObqCQUDGcmgtfK7xZqxct5zu/n4vZv51LfW8fF7NyfLaWQzaGhM2qFRPQRlDsm46rxW/rhX+CrmkkYEBAUCCaXOa+WPO/mtYi6p9ZBRZ2hIKaQSyv3n72R+YmrZc7EOD/Ua9wq/zv93CaOWPQT1CGQUWTJo6lzQNO4Vfp3/7xJGrQKCAoGMKs/4el0LmkIs11DX/7uEUZuAoKIyGUcbist0hS/jij4gKH1UQmhLBs2gK3wVnckw0U4qvzT1VgUDCabfOHpbMmg6Q2anzM1g+PEhszx1CtJ8YwUEM1trZneb2Y+Sf0/rs92imT2cfN05zj5FRtH2DJo6FZ2NU1wn4xm3h3AD8G13Pxf4dvJ9mjl3vyj5unzMfYrk9tSGK7nnws/zytRGHOOVqY3cc+HnWzNkUpchM/VkqjXuHMIVwKXJ49uA7wC/P+Z7ihQiawZNE8fa61J01obJ/5iN20N4m7sfBEj+Xddnu1Vmts/MHjCz3+z3Zma2I9lu36uHXxyzaSL5NfUKtS5DZnXpyTTV0IBgZveY2WMpX1fk2M/b3X0L8FHgP5nZL6Vt5O673H2Lu2/5hTWn53h7kTDqNNaeR12GzNo++V+1oUNG7n5Zv9fM7Gdmtt7dD5rZeuBQn/d4Pvn3GTP7DrAZeHq0JosUp8lXqHUoOtO9kKs17pDRnUAnN/Qa4G96NzCz08zspOTx6cBW4PEx9ytSCF2hVqsuPZmmGndS+bPA183sE8BPgA8DmNkW4Fp3/yTwLuC/mNkxlgLQZ91dAUGipCvUfIqYgK9DT6apxgoI7v4S8Gspz+8DPpk8/j/AhePsR6QsWv4hO91hrXmiX7pCpGy6Qs1GKaLNE+3SFSIStyZPwLeVeggiMlTaXEFdit0kO/UQRGSgfsV6z5zx67UodpPsFBBEZKB+cwXnvHB3rhRRLVoXPw0ZichAg+YK8qwPpYyk+KmHIBKRGK+iQxTrNXVJkKZRQBCJRKwL64VYGE8ZSfWggCASiVivokMsJ6ElQepBcwgikeh/FT3Dp761rtKq6XGL9bQkSD2ohyASiX5XywZRDSGNQovW1YN6CCIpqrhrWtpVdK86Lw2hJUHip4Ag0qOqFMnehfXAsZTtNBErRdGQkUiPKid3n9pwJX/5gR/whQ8dYnZqY+o2moiVoiggiPSIJUWyLvdBlubQkJFIj7IWbRs2T6F7M0jZFBBEepSRIpl1nkITsVImDRmJ9CgjRTLWIjRpN/UQRFIUfWUeyzyFSDf1EEQqoKUcJEYKCCIVUAaRxEhDRiIVUAaRxEgBQaQiyiCS2GjISEREAAUEERFJKCCIiAiggCAiIgkFBBERARQQREQkoYAgIiKAAoKIiCQUEEREBFBAEBGRhAKCiIgACggiIpIYKyCY2YfNbL+ZHTOzLQO2+6CZPWlmB8zshnH2KSIixRi3h/AY8C+B7/bbwMwmgC8B24ALgI+Y2QVj7ldERAIba/lrd38CwMwGbXYJcMDdn0m2/SpwBfD4OPsWEZGwyrgfwgbgp13fzwDvTdvQzHYAO5JvX//0JSc9VnDbQjgdeLHqRmSgdoaldoZVh3bWoY0A54/6g0MDgpndA5yZ8tJOd/+bDPtI6z542obuvgvYlex3n7v3nZeIhdoZltoZltoZTh3aCEvtHPVnhwYEd79s1DdPzABndX2/EXh+zPcUEZHAykg7/T5wrpm9w8zeAlwF3FnCfkVEJIdx005/y8xmgPcD3zKzu5Lnf9HM9gC4+wJwHXAX8ATwdXffn+Htd43TthKpnWGpnWGpneHUoY0wRjvNPXU4X0REWkaVyiIiAiggiIhIIpqAkGMZjB+b2aNm9vA46VWjqstyHWa21szuNrMfJf+e1me7xeRYPmxmpU32Dzs+ZnaSmX0tef17ZraprLb1tGNYOz9mZi90HcNPVtDGW83skJml1u3Yki8k/4cfmtl7ym5j0o5h7bzUzI50HcsbK2jjWWZ2n5k9kfyd/9uUbSo/nhnbmf94unsUX8C7WCqo+A6wZcB2PwZOj7mdwATwNHAO8BbgEeCCktv5x8ANyeMbgM/12e7VCo7h0OMD/B7w5eTxVcDXIm3nx4Avlt22njb8CvAe4LE+r28H9rJUE/Q+4HuRtvNS4H9WfCzXA+9JHk8DT6X8zis/nhnbmft4RtNDcPcn3P3JqtsxTMZ2Hl+uw93fADrLdZTpCuC25PFtwG+WvP9Bshyf7vbvBn7NhqyRUoAYfo9Duft3gZcHbHIFcLsveQBYY2bry2ndmzK0s3LuftDdH0oez7KUGbmhZ7PKj2fGduYWTUDIwYH/ZWYPJktdxChtuY6xf1k5vc3dD8LShwdY12e7VWa2z8weMLOygkaW43N8G19KXT4CvLWU1qW0IdHv9/jbydDBbjM7K+X1qsXweczq/Wb2iJntNbN/WmVDkmHKzcD3el6K6ngOaCfkPJ5lrGV0XIBlMAC2uvvzZrYOuNvM/l9y5RFMmct1jGNQO3O8zduT43kOcK+ZPeruT4dpYV9Zjk8px3CILG34W+AOd3/dzK5lqVfzgcJblk8MxzKLh4Cz3f1VM9sOfBM4t4qGmNkvAN8APu3ur/S+nPIjlRzPIe3MfTxLDQg+/jIYuPvzyb+HzOx/sNStDxoQArSzlOU6BrXTzH5mZuvd/WDSnT3U5z06x/MZM/sOS1caRQeELMens82MmU0Cp1L+cMPQdrr7S13f/jnwuRLalVctlo/pPqG5+x4z+zMzO93dS11QzsxWsnSS/W/u/t9TNonieA5r5yjHs1ZDRma22symO4+Bf87SPRliE8NyHXcC1ySPrwFO6NmY2WlmdlLy+HRgK+UsS57l+HS3/0rgXk9myko0tJ09Y8eXszSWG5s7gd9NsmPeBxzpDCfGxMzO7MwTmdklLJ2fXhr8U8HbYMBfAE+4++f7bFb58czSzpGOZ9mz4wNmzX+Lpcj7OvAz4K7k+V8E9iSPz2Ep0+MRYD9LQzjRtdPfzER4iqWr7Sra+Vbg28CPkn/XJs9vAb6SPP5l4NHkeD4KfKLE9p1wfICbgMuTx6uAvwYOAP8XOKeiz+Wwdv5R8ll8BLgP+CcVtPEO4CAwn3w2PwFcC1ybvG4s3aTq6eT33DeLr+J2Xtd1LB8AfrmCNv4zloZ/fgg8nHxtj+14Zmxn7uOppStERASo2ZCRiIgURwFBREQABQQREUkoIIiICKCAICIiCQUEEREBFBBERCTx/wFpd2WJzsW8DAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(svc, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0, 0], X[y==0, 1])\n",
    "plt.scatter(X[y==1, 0], X[y==1, 1])\n",
    "plt.show() # gamma越小，决策边界越松弛，当很小时，可以认为趋于无穷大成一条直线了，这时就欠拟合了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('std_scaler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('svc',\n",
       "                 SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "                     decision_function_shape='ovr', degree=3, gamma=0.01,\n",
       "                     kernel='rbf', max_iter=-1, probability=False,\n",
       "                     random_state=None, shrinking=True, tol=0.001,\n",
       "                     verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = RBFKernelSVC(gamma=0.01)\n",
    "svc.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: UserWarning: The following kwargs were not used by contour: 'linewidth'\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(svc, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0, 0], X[y==0, 1])\n",
    "plt.scatter(X[y==1, 0], X[y==1, 1])\n",
    "plt.show() # gamma越小，决策边界越松弛，当很小时，可以认为趋于无穷大成一条直线了，这时就欠拟合了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## gamma对模型的的影响是\n",
    "+ gamma取值越大，决策边界越收紧，当很小时，会无限包紧样本点，这时就过拟合了\n",
    "+ gamma取值越小，决策边界越松弛，当很小时，可以认为趋于无穷大成一条直线了，这时就欠拟合了"
   ]
  }
 ],
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